Remote Booking and Scheduling Coordinator
Remote
Coordinate customer reservations and appointments remotely, handling confirmations, changes, and follow-ups. Use online scheduling systems and maintain accurate booking records.
Open
Baseten provides model inference infrastructure for companies deploying machine learning and AI applications. Its platform is built for fast, scalable serving, combining high-throughput inference, rapid deployment, autoscaling, secure enterprise model serving, and support for open-source model packaging. Baseten helps engineering and machine learning teams manage the operational demands of model infrastructure so they can focus on developing domain-specific models. The company operates across artificial intelligence, SaaS, and enterprise technology, with a team of 11–50 employees.
Software Engineer, LLM Inference Platform - Baseten Hybrid
Build Baseten’s distributed LLM inference platform for AI companies. Work across Kubernetes orchestration, model APIs, routing, autoscaling, and production observability.
Description
- Build infrastructure and orchestration for large-scale distributed LLM inference, covering routing, autoscaling, scheduling, and runtime management.
- Develop and operate Model APIs supporting structured outputs, tool and function calling, and multimodal serving.
- Implement API versioning, request validation, usage metering, quotas, and authentication.
- Create instrumentation for metrics, traces, and logs, along with repeatable benchmarks for speed, reliability, and quality.
- Define strong practices for testing, release automation, and operational excellence.
- Diagnose and strengthen production systems across Kubernetes, distributed runtimes, networking, and GPU workloads.
- Collaborate with Inference Performance engineers and partner teams to deliver customer-facing optimizations.
- Lead projects from architecture and deployment through monitoring and iteration informed by customer feedback.
- Balance system performance, reliability, operational simplicity, and developer experience.
Requirements
- Bachelor’s, master’s, or doctoral degree in computer science, engineering, or a related field, or equivalent practical experience.
- At least three years of experience building and operating distributed systems, backend infrastructure, or large-scale APIs where reliability, latency, and scale are critical.
- Demonstrated ownership of low-latency, dependable backend services involving rate limiting, authentication, quotas, metering, and migrations.
- Strong infrastructure judgment, including experience with profiling, tracing, capacity planning, and SLO management.
- Ability to diagnose performance and reliability problems across application, runtime, and infrastructure layers.
- A strong focus on developer experience.
- Willingness to learn new languages, frameworks, and systems, with an interest in inference engineering.
- Excellent written communication and collaboration skills, including clear design documentation and effective cross-functional work.
- Experience with or contributions to LLM inference engines or frameworks such as vLLM, SGLang, TensorRT-LLM, TGI, or Dynamo is a plus.
- Deep Kubernetes experience, including operators and custom resources; familiarity with service meshes or API gateways is a plus.
- Experience with distributed scheduling, autoscaling, or service orchestration is a plus.
- Experience running GPU workloads in production is a plus.
- Background in developer-facing infrastructure or APIs, or contributions to open-source infrastructure or machine-learning systems, is a plus.
- Familiarity with observability tools, CI/CD systems, or release automation is a plus.
Benefits
- Competitive compensation with meaningful equity.
- For U.S. employees and dependents, full coverage of medical, dental, and vision insurance.
- Flexible paid time off, including a company-wide Winter Break.
- Paid parental leave.
- Fertility and family-building support through a Carrot stipend.
- For U.S. employees, a company-facilitated 401(k) plan.
- Exposure to a range of ML startups, creating substantial opportunities for learning and professional networking.
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